Systems and methods for performing tandem mass spectrometry

ABSTRACT

A method of performing tandem mass spectrometry includes supplying a sample to a chromatography column, directing components included in the sample and eluting from the chromatography column to a mass spectrometer, acquiring a series of mass spectra including intensity values of ions produced from the components as a function of m/z of the ions, extracting, from the series of mass spectra, a plurality of detection points representing intensity as a function of time for a selected m/z, estimating, based on the plurality of detection points extracted from the series of mass spectra, a relative position of a selected detection point included in the plurality of detection points, and performing, at the mass spectrometer and based on the estimated relative position, a dependent acquisition for the selected m/z. The relative position of the selected detection point represents a position of the selected detection point relative to an expected reference point.

BACKGROUND INFORMATION

A mass spectrometer is a sensitive instrument that may be used todetect, identify, and/or quantify molecules based on theirmass-to-charge ratio (m/z). A mass spectrometer generally includes anion source for generating ions from components included in the sample, amass analyzer for separating the ions based on their m/z, and an iondetector for detecting the separated ions. The mass spectrometer may beconnected to a computer-based software platform that uses data from theion detector to construct a mass spectrum that shows a relativeabundance of each of the detected ions as a function of m/z. The m/z ofions may be used to detect and quantify molecules in simple and complexmixtures. A separation device such as a liquid chromatograph, gaschromatograph, or capillary electrophoresis device may be coupled to themass spectrometer to separate components included in the sample beforethe components are introduced to the mass spectrometer.

Tandem mass spectrometry is a technique that analyzes ions produced fromthe components in two or more successive stages to acquire mass spectraof precursor ions and/or product ions (e.g., ions produced bydissociation of precursor ions during intermediate dissociation stages).Two-stage tandem mass spectrometry is typically referred to as massspectrometry/mass spectrometry (MS/MS). In a data-dependent acquisition(DDA) procedure, a fixed number of precursor ions whose m/z values wererecorded in a survey acquisition (e.g., a full-spectrum MS scan) areselected, using predetermined rules, for tandem mass spectrometry (e.g.,MS/MS) in which the selected precursor ions are subjected to a one ormore additional stages of mass analysis to generate product ion massspectra.

The DDA procedure makes efficient use of the mass spectrometer'sresources by performing a costly MS/MS analysis on a selected m/z onlywhen the presence of a component of interest eluting from the separationdevice is confirmed by the survey acquisition. In many typical analyses,hundreds of components may elute from the separation device atsubstantially the same time. To enable MS/MS analysis of the largestnumber of components eluting from the separation device, a dynamicexclusion window is applied so that tandem mass spectrometry isperformed for each selected m/z only once for a period of time. However,the predetermined rules typically trigger MS/MS for each selected m/zwhen the corresponding intensity value has just risen above a minimumthreshold and while the intensity value is relatively weak. As a result,longer ion accumulation times are required to achieve an acceptablequality MS/MS spectrum for the selected m/z. However, longeraccumulation times increase the amount of time required for each MS/MSacquisition, and hence fewer components can be analyzed by MS/MS.

SUMMARY

The following description presents a simplified summary of one or moreaspects of the methods and systems described herein in order to providea basic understanding of such aspects. This summary is not an extensiveoverview of all contemplated aspects, and is intended to neitheridentify key or critical elements of all aspects nor delineate the scopeof any or all aspects. Its sole purpose is to present some concepts ofone or more aspects of the methods and systems described herein in asimplified form as a prelude to the more detailed description that ispresented below.

In some illustrative embodiments, an illustrative method of performingtandem mass spectrometry comprises supplying a sample to achromatography column; directing components included in the sample andeluting from the chromatography column to a mass spectrometer; acquiringa series of mass spectra including intensity values of ions producedfrom the components as a function of m/z of the ions; extracting, fromthe series of mass spectra, a plurality of detection points representingintensity as a function of time for a selected m/z; estimating, based onthe plurality of detection points extracted from the series of massspectra, a relative position of a selected detection point included inthe plurality of detection points, the relative position of the selecteddetection point representing a position of the selected detection pointrelative to an expected reference point; and performing, at the massspectrometer and based on the estimated relative position, a dependentacquisition for the selected m/z.

In some illustrative embodiments, the relative position of the selecteddetection point comprises a normalized intensity value of the selecteddetection point, the normalized intensity value representing a ratio ofthe detected intensity value of the selected detection point to anexpected maximum intensity value for the selected m/z.

In some illustrative embodiments, the plurality of detection pointsextracted from the series of mass spectra are included in a slidingwindow, the sliding window including a current detection point.

In some illustrative embodiments, the selected detection point comprisesthe current detection point.

In some illustrative embodiments, the method further comprisesdetermining that the normalized intensity value exceeds a thresholdvalue, wherein the dependent acquisition is performed in response to thedetermining that the normalized intensity value exceeds the thresholdvalue.

In some illustrative embodiments, the threshold value is between about0.5 and about 1.0.

In some illustrative embodiments, the threshold value is between about0.8 and about 1.0.

In some illustrative embodiments, the relative position of the selecteddetection point comprises a temporal distance of the selected detectionpoint to an expected time point for the selected m/z.

In some illustrative embodiments, the relative position of the selecteddetection point comprises a region of an expected elution profile forthe selected m/z and in which the selected detection point is located.

In some illustrative embodiments, the dependent acquisition for theselected m/z comprises an MS/MS acquisition.

In some illustrative embodiments, the performing the dependentacquisition comprises scheduling the dependent acquisition for a futuretime based on the relative position of the selected detection point andperforming the dependent acquisition at the future time.

In some illustrative embodiments, the scheduling the dependentacquisition comprises estimating, based on the estimated relativeposition, an expected time of a maximum intensity value for the selectedm/z, wherein the future time comprises the estimated expected time ofthe maximum intensity value for the selected m/z.

In some illustrative embodiments, an apparatus for performing tandemmass spectrometry comprises a mass spectrometer configured to receivecomponents included in a sample and eluting from a chromatography columnand analyze ions produced from the components, and a computing deviceconfigured to acquire, from the mass spectrometer, a series of massspectra including intensity values of ions produced from the componentsas a function of m/z of the ions; extract, from the series of massspectra, a plurality of detection points detected by the massspectrometer over time for each of a plurality of different selectedm/z; estimate, based on the plurality of detection points for eachrespective selected m/z, a relative position of a selected detectionpoint included in each plurality of detection points, each relativeposition representing a position of the selected detection pointrelative to an expected reference point for the respective selected m/z;and control the mass spectrometer to perform, based on the estimatedrelative positions, a plurality of dependent acquisitions.

In some illustrative embodiments, each estimated relative positioncomprises a normalized intensity value of the respective selecteddetection point, the normalized intensity value representing a ratio ofthe detected intensity value of the selected detection point to anexpected maximum intensity value for the selected m/z, and the computingdevice is configured to control the mass spectrometer to perform theplurality of dependent acquisitions based on a numerical order of theestimated normalized intensity values.

In some illustrative embodiments, the plurality of dependentacquisitions comprises a dependent acquisition for each selected m/z forwhich a corresponding selected detection point has a normalizedintensity value exceeding a threshold value.

In some illustrative embodiments, the controlling the mass spectrometerto perform the plurality of dependent acquisitions comprises schedulingeach of the plurality of dependent acquisitions for a different futuretime based on a numerical order of the normalized intensity values andcontrolling the mass spectrometer to perform each of the plurality ofdependent acquisitions at the respective future time.

In some illustrative embodiments, the scheduling each of the pluralityof dependent acquisitions comprises estimating, based on the estimatednormalized intensity values, an expected time of a maximum intensityvalue for each selected m/z, wherein each respective future timecomprises the estimated expected time of the maximum intensity value forthe respective selected m/z.

In some illustrative embodiments, a non-transitory computer-readablemedium stores instructions that, when executed, cause a processor of acomputing device to acquire a first data set comprising a series of massspectra including intensity values of ions produced from analyteseluting from a chromatography column as a function of m/z of the ions;extract a second data set from the first data set, the second data setincluding a plurality of detection points representing intensity as afunction of time for a selected mass-to-charge ratio (m/z); estimate,based on the second data set, a relative position of a selecteddetection point included in the second data set, the relative positionof the selected detection point representing a position of the selecteddetection point relative to an expected reference point for the selectedm/z; and control, based on the estimated relative position, the massspectrometer to perform a data-dependent action.

In some illustrative embodiments, the data-dependent action comprisesperforming tandem mass spectrometry.

In some illustrative embodiments, the controlling the mass spectrometerto perform the data-dependent action comprises scheduling the massspectrometer to perform tandem mass spectrometry for the selected m/z ata future time.

In some illustrative embodiments, the scheduling the mass spectrometerto perform tandem mass spectrometry for the selected m/z at the futuretime comprises estimating, based on the estimated relative position ofthe selected detection point, an expected time of a maximum intensityvalue for the selected m/z, wherein the future time comprises theestimated expected time of the maximum intensity value for the selectedm/z.

In some illustrative embodiments, a system comprises a chromatographycolumn configured to receive a sample and separate components includedin the sample; a mass spectrometer configured to receive the componentseluting from the chromatography column and analyze ions produced fromthe components; and a computing device configured to acquire a series ofmass spectra including intensity values of ions produced from thecomponents as a function of m/z of the ions; extract, from the series ofmass spectra, a plurality of detection points representing intensity asa function of time for a selected m/z; estimate, based on the pluralityof detection points extracted from the series of mass spectra, arelative position of a selected detection point included in theplurality of detection points, the relative position of the selecteddetection point representing a position of the selected detection pointrelative to an expected reference point, and control, based on theestimated relative position, the mass spectrometer to perform adependent acquisition for the selected m/z.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying drawings illustrate various embodiments and are a partof the specification. The illustrated embodiments are merely examplesand do not limit the scope of the disclosure. Throughout the drawings,identical or similar reference numbers designate identical or similarelements.

FIG. 1 shows an illustrative liquid chromatography-mass spectrometrysystem including a liquid chromatograph and a mass spectrometer.

FIG. 2 shows an illustrative implementation of the mass spectrometer ofFIG. 1.

FIG. 3 shows an illustrative mass chromatogram of a selected m/z (e.g.,an extracted ion chromatogram (XIC)) associated with ions produced froma component that is included in a sample and that elutes from the liquidchromatograph of FIG. 1.

FIG. 4 shows an illustrative mass spectrometry control system.

FIG. 5 shows an illustrative method of performing tandem massspectrometry with the liquid chromatography-mass spectrometry system ofFIG. 1 and the mass spectrometry control system of FIG. 4.

FIG. 6 shows an illustrative XIC that may be generated from, or isrepresentative of, data acquired by the liquid chromatography-massspectrometry system of FIG. 1 during a plurality of survey acquisitions.

FIG. 7 shows the illustrative XIC of FIG. 6 after the acquisition of twoadditional survey acquisitions.

FIG. 8 shows an illustrative division of an elution profile of the XICshown in FIG. 7 into distinct regions.

FIG. 9 shows another illustrative division of the elution profile of theXIC shown in FIG. 7 into distinct regions.

FIG. 10 shows another illustrative method of performing tandem massspectrometry with the liquid chromatography-mass spectrometry system ofFIG. 1 and the mass spectrometry control system of FIG. 4.

FIG. 11 shows an illustrative method for training a machine learningmodel that may implement an estimation model.

FIG. 12 shows an illustrative XIC that may be generated from, or isrepresentative of, training data that may be used for training themachine learning model of FIG. 11.

FIG. 13 shows an illustrative computing device.

DETAILED DESCRIPTION

Methods and systems for performing tandem mass spectrometry aredescribed herein. In some illustrative embodiments, a method ofperforming tandem mass spectrometry includes supplying a sample to achromatography column, directing components included in the sample andeluting from the chromatography column to a mass spectrometer, andacquiring a series of mass spectra. The mass spectra include intensityvalues of ions produced from the components as a function of m/z of theions. A plurality of detection points are extracted from the series ofmass spectra. The plurality of detection points represent intensity as afunction of time (e.g., retention time) for a selected m/z. Based on theplurality of detection points extracted from the series of mass spectra,a normalized intensity value of a selected detection point included inthe plurality of detection points is estimated. The normalized intensityvalue represents a ratio of the detected intensity value of the selecteddetection point to a reference intensity value (e.g., an expectedmaximum intensity value for the selected m/z). Based on the estimatednormalized intensity value, a dependent acquisition for the selected m/zis performed. For example, tandem mass spectrometry (e.g., MS/MS) may beperformed in response to a determination that the estimated normalizedintensity value for the selected detection point exceeds a thresholdvalue (e.g., 0.7).

The systems and methods described herein may provide various benefits,which may include one or more advantages over conventional systems andmethods for performing tandem mass spectrometry. For example, adependent acquisition or other data-dependent action may be triggeredwhen the current signal intensity of a particular component is at ornear the maximum intensity level (e.g., at about 70% or higher of themaximum intensity level) of the expected elution profile of thecomponent. As a result, the dependent acquisition may be acquired withhigh quality (e.g., a high signal-to-noise ratio). Furthermore, a highquality dependent acquisition signal allows the mass analyzer and/or iontrap accumulation time to be shorter than when the dependent acquisitionsignal is of lower quality, thereby decreasing the time required toperform a dependent acquisition and allowing a greater number ofco-eluting components to be analyzed by tandem mass spectrometry.

Various embodiments will now be described in more detail with referenceto the figures. The systems and methods described herein may provide oneor more of the benefits mentioned above and/or various additional and/oralternative benefits that will be made apparent herein.

In some implementations, the methods and systems for performing tandemmass spectrometry may be used in conjunction with a combinedseparation-mass spectrometry system, such as a liquidchromatography-mass spectrometry (LC-MS) system. As such, an LC-MSsystem will now be described. The described LC-MS system is illustrativeand not limiting. The methods and systems described herein may operateas part of or in conjunction with the LC-MS system described hereinand/or with any other suitable separation-mass spectrometry system,including a high-performance liquid chromatography-mass spectrometry(HPLC-MS) system, a gas chromatography-mass spectrometry (GC-MS) system,or a capillary electrophoresis-mass spectrometry (CE-MS) system.

FIG. 1 shows an illustrative LC-MS system 100. LC-MS system 100 includesa liquid chromatograph 102, a mass spectrometer 104, and a controller106. Liquid chromatograph 102 is configured to separate, over time,components (e.g., analytes) within a sample 108 that is injected intoliquid chromatograph 102. Sample 108 may include, for example, chemicalcomponents (e.g., molecules, ions, etc.) and/or biological components(e.g., metabolites, proteins, lipids, etc.) for detection and analysisby LC-MS system 100. Liquid chromatograph 102 may be implemented by anyliquid chromatograph as may suit a particular implementation. In liquidchromatograph 102, sample 108 may be injected into a mobile phase (e.g.,a solvent), which carries sample 108 through a column 110 containing astationary phase (e.g., an adsorbent packing material). As the mobilephase passes through column 110, components within sample 108 elute fromcolumn 110 at different times based on, for example, their size, theiraffinity to the stationary phase, their polarity, and/or theirhydrophobicity. A detector (e.g., a spectrophotometer) may measure therelative intensity of a signal modulated by each separated component ineluate 112 from column 110. Data generated by the detector may berepresented as a chromatogram, which plots retention time on the x-axisand a signal representative of the relative intensity on the y-axis. Theretention time of a component is generally measured as the period oftime between injection of sample 108 into the mobile phase and therelative intensity peak maximum after chromatographic separation. Insome examples, the relative intensity may be correlated to orrepresentative of relative abundance of the separated components. Datagenerated by liquid chromatograph 102 may be output to controller 106.

In some cases, particularly in analyses of complex mixtures, multipledifferent components in sample 108 may co-elute from column 110 atapproximately the same time, and thus may have the same or similarretention times. As a result, determination of the relative intensity ofthe individual components within sample 108 requires further separationof the individual components. To this end, liquid chromatograph 102directs components included in eluent 112 to mass spectrometer 104.

Mass spectrometer 104 is configured to ionize the components receivedfrom liquid chromatograph 102 and sort or separate the produced ionsbased on m/z of the ions. A detector in mass spectrometer 104 measuresthe intensity of the signal produced by the ions. As used herein,“intensity” or “signal intensity” may refer to any suitable metric, suchas abundance, relative abundance, ion count, intensity, relativeintensity, etc. Data generated by the detector may be represented bymass spectra, which plot the intensity of the observed signal as afunction of m/z of the ions. Data acquired by mass spectrometer 104 maybe output to controller 106.

Mass spectrometer 104 may be implemented by any suitable massspectrometer, such as a tandem mass spectrometer configured to performtandem mass spectrometry (e.g., MS/MS), a multi-stage mass spectrometerconfigured to perform multi-stage mass spectrometry (also denoted MSn),a hybrid mass spectrometer, and the like. FIG. 2 shows an illustrativeimplementation of mass spectrometer 104. As shown, mass spectrometer 104is tandem-in-space (e.g., has multiple mass analyzers) and has twostages for performing MS/MS. However, mass spectrometer 104 is notlimited to this configuration but may have any other suitableconfiguration. For example, mass spectrometer 104 may have a single massanalyzer and may be tandem-in-time. Additionally or alternatively, massspectrometer 104 may be a multi-stage mass spectrometer and may have anysuitable number of stages (e.g., three or more) for performingmulti-stage tandem mass spectrometry (e.g., MS/MS/MS).

As shown, mass spectrometer 104 includes an ion source 202, a first massanalyzer 204-1, a collision cell 204-2, a second mass analyzer 204-3,and a controller 206. Mass spectrometer 104 may further include anyadditional or alternative components not shown as may suit a particularimplementation (e.g., ion optics, filters, an autosampler, a detector,etc.).

Ion source 202 is configured to produce a stream 208 of ions from thecomponents and deliver the ions to first mass analyzer 204-1. Ion source202 may use any suitable ionization technique, including withoutlimitation electron ionization, chemical ionization, matrix assistedlaser desorption/ionization, electrospray ionization, atmosphericpressure chemical ionization, atmospheric pressure photoionization,inductively coupled plasma, and the like. Ion source 202 may includevarious components for producing ions from components included in sample108 and delivering the ions to first mass analyzer 204-1.

First mass analyzer 204-1 is configured to receive ion stream 208 anddirect a beam 210 of ions (e.g., precursor ions) to collision cell204-2. Collision cell 204-2 is configured to receive beam 210 of ionsand produce product ions (e.g., fragment ions) via controlleddissociation processes. Collision cell 204-2 is further configured todirect a beam 212 of product ions to second mass analyzer 204-3. Secondmass analyzer 204-3 is configured to filter and/or perform a massanalysis of the product ions.

Mass analyzers 204-1 and 204-3 are configured to separate ions accordingto m/z of each of the ions. Mass analyzers 204-1 and 204-3 may beimplemented by any suitable mass analyzer, such as a quadrupole massfilter, an ion trap (e.g., a three-dimensional quadrupole ion trap, acylindrical ion trap, a linear quadrupole ion trap, a toroidal ion trap,etc.), a time-of-flight (TOF) mass analyzer, an electrostatic trap massanalyzer (e.g. an orbital electrostatic trap such as an Orbitrap massanalyzer, a Kingdon trap, etc.), a Fourier transform ion cyclotronresonance (FT-ICR) mass analyzer, a sector mass analyzer, and the like.Mass analyzers 204 need not be implemented by the same type of massanalyzer.

Collision cell 204-2 may be implemented by any suitable collision cell.As used herein, “collision cell” may encompass any structure or deviceconfigured to produce product ions via controlled dissociation processesand is not limited to devices employed for collisionally-activateddissociation. For example, collision cell 204-2 may be configured tofragment precursor ions using collision induced dissociation, electrontransfer dissociation, electron capture dissociation, photo induceddissociation, surface induced dissociation, ion/molecule reactions, andthe like.

An ion detector (not shown) is configured to detect ions at each of avariety of different m/z and responsively generate an electrical signalrepresentative of ion intensity. The electrical signal is transmitted tocontroller 206 for processing, such as to construct a mass spectrum ofthe sample. For example, mass analyzer 204-3 may emit an emission beamof separated ions to the ion detector, which is configured to detect theions in the emission beam and generate or provide data that can be usedby controller 206 to construct a mass spectrum of the sample. The iondetector may be implemented by any suitable detection device, includingwithout limitation an electron multiplier, a Faraday cup, and the like.

Controller 206 may be communicatively coupled with, and configured tocontrol operations of, mass spectrometer 104. For example, controller206 may be configured to control operation of various hardwarecomponents included in ion source 104 and/or mass analyzers 204-1 and204-3. To illustrate, controller 206 may be configured to control anaccumulation time of ion source 202 and/or mass analyzers 204, controlan oscillatory voltage power supply and/or a DC power supply to supplyan RF voltage and/or a DC voltage to mass analyzers 204, adjust valuesof the RF voltage and DC voltage to select an effective m/z (including amass tolerance window) for analysis, and adjust the sensitivity of theion detector (e.g., by adjusting the detector gain).

Controller 206 may also include and/or provide a user interfaceconfigured to enable interaction between a user of mass spectrometer 104and controller 206. The user may interact with controller 206 via theuser interface by tactile, visual, auditory, and/or other sensory typecommunication. For example, the user interface may include a displaydevice (e.g., liquid crystal display (LCD) display screen, a touchscreen, etc.) for displaying information (e.g., mass spectra,notifications, etc.) to the user. The user interface may also include aninput device (e.g., a keyboard, a mouse, a touchscreen device, etc.)that allows the user to provide input to controller 206. In otherexamples the display device and/or input device may be separate from,but communicatively coupled to, controller 206. For instance, thedisplay device and the input device may be included in a computer (e.g.,a desktop computer, a laptop computer, etc.) communicatively connectedto controller 206 by way of a wired connection (e.g., by one or morecables) and/or a wireless connection.

Controller 206 may include any suitable hardware (e.g., a processor,circuitry, etc.) and/or software as may serve a particularimplementation. While FIG. 2 shows that controller 206 is included inmass spectrometer 104, controller 206 may alternatively be implementedin whole or in part separately from mass spectrometer 104, such as by acomputing device communicatively coupled to mass spectrometer 104 by wayof a wired connection (e.g., a cable) and/or a network (e.g., a localarea network, a wireless network (e.g., Wi-Fi), a wide area network, theInternet, a cellular data network, etc.). In some examples, controller206 may be implemented in whole or in part by controller 106.

Referring again to FIG. 1, controller 106 may be communicatively coupledwith, and configured to control operations of, LC-MS system 100 (e.g.,liquid chromatograph 102 and mass spectrometer 104). Controller 106 mayinclude any suitable hardware (e.g., a processor, circuitry, etc.)and/or software configured to control operations of and/or interfacewith the various components of LC-MS system 100 (e.g., liquidchromatograph 102 or mass spectrometer 104).

For example, controller 106 may be configured to acquire, from massspectrometer 104, a first data set comprising data acquired over time byliquid chromatograph 102 and mass spectrometer 104. The first data setmay include a series of mass spectra including intensity values of ionsproduced from the components of sample 108 as a function of m/z of theions. The first data set may be represented in a three-dimensional mapin which time (e.g., retention time) is plotted along an x-axis, m/z isplotted along a y-axis, and intensity is plotted along a z-axis.Spectral features on the map (e.g., peaks of intensity) representdetection by LC-MS system 100 of ions produced from various componentsincluded in sample 108. The x-axis and z-axis of the map may be used togenerate a mass chromatogram which plots intensity as a function oftime. The y-axis and z-axis of the map represent mass spectra that plotintensity as a function of m/z.

FIG. 3 shows an illustrative mass chromatogram 300 (e.g., an extractedion chromatogram (XIC)) of a selected m/z associated with ions producedfrom a component included in sample 108 and that elutes from liquidchromatograph 102. As used herein, a “selected m/z” may include aspecific m/z with or without a mass tolerance window or a narrow rangeof m/z. Mass chromatogram 300 is generated from data acquired by massspectrometer 104 during a first stage of a DDA procedure, such as aplurality of survey acquisitions (e.g., MS full-spectrum scans) or massspectra acquisitions. Mass chromatogram 300 plots intensity (arbitraryunits) as a function of retention time (in minutes). As shown, masschromatogram 300 includes a plurality of detection points, each acquiredfrom a different acquisition, that together form an elution profile 302of the component (as indicated by the dashed line curve). As thecomponent elutes from column 110, the detected intensity of the ionsproduces a peak 304 having a roughly Gaussian profile.

In a conventional DDA experiment, a dependent acquisition (e.g., anMS/MS scan) is triggered when elution of the component is detected. Asshown in FIG. 3, elution of the component is detected at a time t₁ whenthe detected intensity value has just risen above a predeterminedminimum threshold intensity value (indicated by dashed line 306), whichtypically occurs at the start of peak 304 and while the intensity valueis relatively weak. Dynamic exclusion is applied during a time window308 (e.g., from time t₁ to time t₂ or for the duration of the peak) sothat a dependent acquisition is not performed for the selected m/zduring the time window 308. To maximize the probability of matching theMS/MS spectra to a known component when the dependent acquisition istriggered early in peak 304, longer ion accumulation times are requiredat the mass analyzer to produce a stronger MS/MS signal for eachselected m/z. However, longer ion accumulation times results in slowerMS/MS acquisitions. As a result, fewer components of different selectedm/z can be analyzed by MS/MS.

These issues may be addressed by triggering a dependent acquisition(e.g., an MS/MS scan) for the selected m/z when the detected intensityvalue is at or near the apex 310 of peak 304. However, determiningwhether the selected m/z is at or near apex 310 of peak 304 is achallenging signal processing problem. Previous attempts to solve theproblem treated survey acquisition signals for each selected m/z as sinewaves. With this technique, a Fourier analysis is performed on the dataso that each point is assigned a frequency and phase value. When thephase falls within a certain range of values corresponding to the apexof the elution profile peak, a data dependent action can be taken. Thisprocedure theoretically works well, but produces random results withreal, noisy data. A better method is needed to initiate a data-dependentaction at or near the apex of an elution profile peak for a selectedm/z.

As will be described below in more detail, an improved method ofperforming tandem mass spectrometry includes estimating, based oncurrently acquired mass spectra, a relative position of a selecteddetection point in an elution profile and performing, based on theestimated relative position of the selected detection point, adata-dependent action. A relative position of a selected detection pointmay be the position of the selected detection point, in time orintensity, relative to a reference point in the elution profile (e.g.,the apex). Additionally or alternatively, the relative position may be aregion of the elution profile in which the selected detection point islocated and that indicates a state of elution of the component (e.g., abaseline region, a rising region, an apex region, a falling region,etc.). The regions may be defined relative to a reference point in theelution profile (e.g., the apex).

Estimation of the relative position of the selected detection point isbased on the principle that the relative position of the selecteddetection point is a function of detected intensity values of aplurality of detection points around (e.g., preceding and/or following)the selected detection point. Thus, the position of the selecteddetection point relative to the expected or predicted apex of theelution profile peak may be estimated in real-time and used to perform adata-dependent action.

One or more operations associated with an improved method of performingtandem mass spectrometry may be performed by a mass spectrometry controlsystem. FIG. 4 shows an illustrative mass spectrometry control system400 (“system 400”). System 400 may be implemented entirely or in part byLC-MS system 100 (e.g., by controller 106 and/or controller 206).Alternatively, system 400 may be implemented separately from LC-MSsystem 100.

System 400 may include, without limitation, a storage facility 402 and aprocessing facility 404 selectively and communicatively coupled to oneanother. Facilities 402 and 404 may each include or be implemented byhardware and/or software components (e.g., processors, memories,communication interfaces, instructions stored in memory for execution bythe processors, etc.). In some examples, facilities 402 and 404 may bedistributed between multiple devices and/or multiple locations as mayserve a particular implementation.

Storage facility 402 may maintain (e.g., store) executable data used byprocessing facility 404 to perform any of the operations describedherein. For example, storage facility 402 may store instructions 406that may be executed by processing facility 404 to perform any of theoperations described herein. Instructions 406 may be implemented by anysuitable application, software, code, and/or other executable datainstance.

Storage facility 402 may also maintain any data acquired, received,generated, managed, used, and/or transmitted by processing facility 404.For example, storage facility 402 may maintain LC-MS data (e.g.,acquired chromatogram data and/or mass spectra data) and/or estimationdata. Estimation data may include data representative of, used by, orassociated with one or more models (e.g., machine learning models) oralgorithms maintained by processing facility 404 for estimating arelative position of a selected detection point included in the LC-MSdata.

Processing facility 404 may be configured to perform (e.g., executeinstructions 406 stored in storage facility 402 to perform) variousprocessing operations described herein. It will be recognized that theoperations and examples described herein are merely illustrative of themany different types of operations that may be performed by processingfacility 404. In the description herein, any references to operationsperformed by system 400 may be understood to be performed by processingfacility 404 of system 400. Furthermore, in the description herein, anyoperations performed by system 400 may be understood to include system400 directing or instructing another system or device to perform theoperations.

FIG. 5 shows an illustrative method of performing tandem massspectrometry. While FIG. 5 shows illustrative operations according toone embodiment, other embodiments may omit, add to, reorder, and/ormodify any of the operations shown in FIG. 5. One or more of theoperations shown in FIG. 5 may be performed by LC-MS system 100 and/orsystem 400, any components included therein, and/or any implementationsthereof.

In operations 502 and 504, liquid chromatograph 102 supplies sample 108to column 110 and directs components included in sample 108 and thatelute from column 110 to mass spectrometer 104. In operation 506, massspectrometer 104 performs a first stage of a DDA procedure (e.g.,full-spectrum mass spectra acquisitions, MS survey scans, etc.) as thecomponents elute from column 110 and acquires a first data set 508(e.g., LC-MS data) that includes a series of mass spectra includingintensity values of ions produced from the components as a function ofm/z of the ions.

In operation 510, system 400 acquires first data set 508 from LC-MSsystem 100 (e.g., mass spectrometer 104).

In operation 512, system 400 extracts a second data set 514 from firstdata set 508. Second data set 514 includes a plurality of detectionpoints each from a different acquisition and representing intensity, asdetected by mass spectrometer 104, as a function of time for a selectedm/z. The selected m/z may be the m/z of ions produced from a particularcomponent of interest included in sample 108 and may be selected basedon mass peaks present in first data set 508. In some examples, seconddata set 514 comprises an XIC or source data that may be used togenerate an XIC for the selected m/z.

In operation 516, system 400 estimates, based on second data set 514 andan estimation model 518, a relative position of a selected detectionpoint included in the second data set. The relative position of theselected detection point may be a normalized intensity value of theselected detection point, a temporal distance of the selected detectionpoint from an expected time of a reference point, or a region of theelution profile in which the selected detection point is located (e.g.,a baseline region, a rising region, an apex region, a falling region,etc.), where the regions are defined relative to a reference point inthe elution profile (e.g., the apex). Operation 516 may be performed inany suitable way. Illustrative embodiments of operation 516 will now bedescribed with reference to FIGS. 6-9.

FIG. 6 shows an illustrative XIC 600 that may be generated from, or isrepresentative of, second data set 514. XIC 600 will facilitatedescription of operation 516, but operation 516 may be performed withraw source data without generating XIC 600. As shown, XIC 600 plots aplurality of detection points 602 each representing a detected intensityvalue (arbitrary units) of the selected m/z, as detected by massspectrometer 104 during a first stage of a DDA procedure (e.g., duringoperation 506), as a function of retention time (in minutes). Eachsuccessive mass spectrum acquisition by mass spectrometer 104 adds a newdetection point 602 to XIC 600. As shown on XIC 600, the right-mostdetection point 602-1 is a current (most-recent) detection point 602acquired at current time t_(c) and having a current intensity valuel_(c), as indicated by dashed line 604. As shown in FIG. 6, detectionpoints 602 together form an elution profile of the component associatedwith the selected m/z. The upward trajectory of intensity values ofdetection points 602 indicates the start of a peak 606 in the expected(e.g., estimated or predicted) elution profile. In FIG. 6, the expectedelution profile is represented by a dashed-line curve.

In some examples, system 400 is configured to estimate a normalizedintensity value of a selected detection point 602 relative to areference intensity value. In the examples that follow, the selecteddetection point 602 is the current detection point 602-1. In alternativeexamples, the selected detection point may be any historical detectionpoint 602 acquired during the DDA procedure (e.g., any detection point602 acquired prior to current time t_(c), such as a second or thirdmost-recent detection point 602).

In some examples, the reference intensity value is an expected maximumintensity value l_(max) at apex 608 of peak 606 for the selected m/z, asindicated by dashed line 610. However, since the maximum intensity valuel_(max) of peak 606 has not yet been detected at current time t_(c),intensity values for a distinct set 612 of a plurality of detectionpoints 602 are applied as inputs to estimation model 518, which isconfigured to estimate the normalized intensity value of the selecteddetection point 602-1. In some examples, set 612 comprises apredetermined number (e.g., 6, 24, 48, 100, etc.) of detection points602. In alternative examples, set 612 comprises only detection points602 occurring within a sliding time window. For example, the slidingtime window may encompass a period of 0.1 seconds, 0.5 seconds, 3seconds, etc. In either configuration, the selected detection point 602is included in the set 612.

In some examples, detection points 602 may not be evenly spaced alongthe time axis. To simplify processing of second data set 514, detectionpoints 602 may be corrected (such as by interpolation) to a fixed anduniform time spacing (e.g., 1 second).

Estimation model 518 is configured to perform any suitable heuristic,process, and/or operation that may be performed or executed by system400 to estimate a normalized intensity value of the selected detectionpoint 602-1. In some examples, estimation model 518 may be implementedby hardware and/or software components (e.g., processors, memories,communication interfaces, instructions stored in memory for execution bythe processors, etc.), such as storage facility 402 (e.g., estimationdata) and/or processing facility 404 of system 400. Estimation model 518may include any suitable algorithm and/or machine learning modelconfigured to estimate a normalized intensity value of a selecteddetection point based on intensity values for a set of historicaldetection points. Estimation model 518 may estimate the normalizedintensity value in any suitable way. In some examples, estimation model518 comprises a machine learning model. An illustrative machine learningmodel, and methods of training the machine learning model, will bedescribed below in more detail.

When the reference intensity value is the expected maximum intensityvalue l_(max) of peak 606, the normalized intensity value of selecteddetection point 602-1 will generally range from 0 to 1. In the exampleof FIG. 6, system 400 may estimate the normalized intensity value ofselected detection point 602-1 to be 0.15, thus indicating that thecurrent intensity value l_(c) of selected detection point 602-1 is about15% of the expected intensity value l_(max) at apex 608. FIG. 7 showsXIC 600 after data from two additional acquisitions. In FIG. 7, system400 now estimates the normalized intensity value of the currentdetection point 602-1 to be 0.65.

In the examples described above, the reference intensity value is theexpected maximum intensity value l_(max) at apex 608 of peak 606.However, any other normalization scheme may be used, and the referenceintensity value may be any other suitable reference value, such as aknown running average intensity value for the selected m/z, a globalmaximum intensity value for multiple different m/z, a recent maximumintensity value for the selected m/z, etc.

In the examples just described, the relative position of selecteddetection point 602-1 is the normalized intensity value of selecteddetection point 602-1. However, as mentioned above, in other examplesthe relative position of selected detection point 602-1 may be atemporal distance of selected detection point 602-1 to a referencepoint.

In some examples, as shown in FIGS. 6 and 7, the reference point mayoccur at an expected time t_(max) at which the intensity value of peak606 is expected to reach maximum intensity value l_(max) at apex 608.System 400 may estimate the temporal distance (e.g., a differencebetween t_(max) and t_(c)) of the selected detection point 602-1 to thereference point in any suitable way. In some examples, the temporaldistance is estimated in a manner similar to estimating the normalizedintensity value of selected detection point 602-1. For example, set 612of detection points 602 and/or the estimated normalized intensity valuesare applied as inputs to estimation model 518, which is additionally oralternatively configured to estimate expected time t_(max) based on aset of historical detection points. As shown in FIG. 6, system 400 mayestimate, based on set 612, a temporal distance of selected detectionpoint 602-1 to be 0.05 minutes (e.g., 3 seconds). As shown in FIG. 7,system 400 may estimate, based on set 612, the temporal distance ofselected detection point 602-1 to be 0.02 minutes (e.g., 1.2 seconds).

In yet other examples, the relative position of selected detection point602-1 is a region of the expected elution profile in which selecteddetection point 602-1 is located. FIG. 8 shows an illustrative divisionof the elution profile of XIC 600 shown in FIG. 7 into distinct regions802 (e.g., regions 802-1 through 802-3). While FIG. 8 shows threedistinct regions 802, any other suitable number of regions may be used.

As shown in FIG. 8, regions 802 are divided based on normalizedintensity values and relative to a reference intensity point in theelution profile (e.g., apex 608). For example, a first region 802-1 (a“baseline region”) is located below a first threshold normalizedintensity value 804-1 (e.g., 0.1). A second region 802-2 (a “changingregion”) is located between first threshold normalized intensity value804-1 and a second threshold normalized intensity value 804-2 (e.g.,0.8). A third region 802-3 (an “apex region”) is located above thesecond threshold normalized intensity value 804-2. The configuration ofregions 802 shown in FIG. 8 is merely illustrative, as any otherconfiguration may be used. For example, regions 802 may be furtherdefined based on a direction of change of the normalized intensityvalue. For example, a portion of region 802-2 on the left side of apex608 (or prior to expected time t_(max)) may be a “rising region” and aportion of region 802-2 on the right side of apex 608 (or after expectedtime t_(max)) may be a “falling region.”

In additional or alternative examples, the elution profile may bedivided into regions based on time and relative to a reference point inthe elution profile (e.g., expected time t_(max)). FIG. 9 shows anillustrative division of the elution profile of XIC 600 shown in FIG. 7into distinct regions 902 (e.g., regions 902-1 through 902-5). WhileFIG. 9 shows five distinct regions 902, any other suitable number ofregions may be used. As shown, a first region 902-1 (a “baselineregion”) is located prior to a first threshold temporal distance 904-1before expected time t_(max) (e.g., 0.6 min). A second region 902-2 (a“rising region”) is located between first threshold temporal distance904-1 and a second threshold temporal distance 904-2 before expectedtime t_(max) (e.g., 0.2 min). A third region 902-3 (an “apex region”) islocated between second threshold temporal distance 904-2 and a thirdthreshold temporal distance 904-3 after expected time t_(max) (e.g., 0.2min). Generally, an apex region encompasses apex 608. A fourth region902-4 (a “falling region”) is located between third threshold temporaldistance 904-3 and a fourth threshold temporal distance 904-4 afterexpected time t_(max) (e.g., 1.4 min). A fifth region 902-5 (a “baselineregion”) is located after fourth threshold temporal distance 904-4. Theconfiguration of regions 902 shown in FIG. 9 is merely illustrative, asany other configuration may be used. For example, any one or moreregions 902 may be defined relative to another reference point, such asan expected time t_(min) at which the intensity value is expected toreach a minimum value l_(min).

System 400 may estimate a region 802 or 902 in which selected detectionpoint 602-1 is located in any suitable way. In some examples, systemapplies set 612 to estimation model 518, which is configured to classifyselected detection point 602-1 according to the region in which it islocated. In the example of FIG. 8, system 400 may estimate, based on set612 and estimation model 518, that selected detection point 602-1 islocated within region 802-2. In the example of FIG. 9, system 400 mayestimate, based on set 612 and estimation model 518, that selecteddetection point 602-1 is located within region 902-2.

Referring again to FIG. 5, in operation 520 a data-dependent action isperformed based on the estimated relative position of the selecteddetection point (e.g., the selected detection point 602-1). Operation520 may be performed in any suitable way.

In some examples, the data-dependent action comprises performing adependent acquisition. For example, mass spectrometer 104 may perform adependent acquisition (e.g., tandem mass spectrometry, such as an MS/MSscan). The dependent acquisition may be based on the estimated relativeposition in any suitable way.

In some examples, system 400 may compare the estimated normalizedintensity value with a threshold value. The threshold value may be anysuitable value, such as a value between about 0.5 and about 1.0, a valuebetween about 0.8 and about 1.0, and/or any other suitable value. Massspectrometer 104 may perform the dependent acquisition in response to adetermination that the estimated normalized intensity value of theselected m/z exceeds the threshold value.

Triggering of a dependent acquisition based on an estimated normalizedintensity value will now be explained with reference to FIGS. 6 and 7.If the threshold value is set at 0.6, the current detection point 602-1in FIG. 6 has a normalized intensity value of 0.15 and thus does nottrigger performing a dependent acquisition. In FIG. 7, the normalizedintensity value of the current detection point 602-1 is now 0.65 andthus triggers a dependent acquisition.

Various alternative triggering schemes may be used based on one or moreestimated normalized intensity values. For example, a dependentacquisition may be triggered by a determination that successivenormalized intensity values estimated by system 400 follow a particularsequence. For example, the particular sequence may include a normalizedintensity value less than, followed by a normalized intensity value near1 (e.g., within a predetermined tolerance of 1, such as above 0.8),followed by a normalized intensity value less than 1 again. Anotheralternative triggering scheme may be based on a rate of change ofestimated normalized intensity value for the selected m/z. For example,a dependent acquisition may be triggered when the rate of change of theestimated normalized intensity value (e.g., the slope of a curve whenthe estimated normalized intensity value is plotted as a function oftime) is below a threshold value.

Triggering of a dependent acquisition may also be based on an estimatedtemporal distance. For example, system 400 may compare the estimatedtemporal distance of the selected detection point with a thresholdvalue. The threshold value may be any suitable value, such as a valuebetween about 0.1 minutes and about 1 minute, a value between about 0.1and about 0.5 minutes, and/or any other suitable value. Massspectrometer 104 may perform the dependent acquisition in response to adetermination that the estimated temporal distance of the selecteddetection point is less than the threshold value.

Triggering of a dependent acquisition may also be based on an estimatedregion in which the selected detection point is located. For example,mass spectrometer 104 may perform the dependent acquisition in responseto a determination that the selected detection point is located within arising region, an apex region, and/or a falling region.

With the triggering schemes described above, the dependent acquisitionis performed when the intensity of the ions produced from the componentof interest are at or near the apex of the component's elution profile.As a result, a relatively short ion accumulation time may be set for thedependent acquisition and a greater number of dependent acquisitions maybe acquired. That is, the limit of detection of components included inthe sample is improved because there is a maximum amount of time thatmay be spent accumulating ions for an analysis, limited on the upper endby the width of the elution peak and the capacity of the ion storagedevice. If an MS/MS mass spectrum is acquired when the flux of the ionsis higher, a higher quality mass spectrum with higher signal-to-noiseratio will be acquired in a shorter amount of time.

In further examples, the data-dependent action comprises scheduling afuture performance of a dependent acquisition for the selected m/z. Forexample, system 400 may schedule the dependent acquisition to beperformed at estimated expected time t_(max) or at any other suitabletime. In this way, a dependent acquisition may be scheduled andsubsequently performed when the intensity value of detected ions is ator near the maximum intensity value.

In some examples, a scheduling action may be based on relative positioncriteria. For example, system 400 may schedule a future performance of adependent acquisition in response to a determination that the estimatednormalized intensity value is less than a threshold value, the estimatedtemporal distance is greater than a threshold time value, and/or theselected detection point is not located in a particular region. System400 may also initiate an immediate dependent acquisition in response toa determination that the estimated normalized intensity value exceedsthe threshold value, the estimated temporal distance is less than thethreshold time value, and/or the selected detection point is located inthe particular region. In some examples, system 400 may schedule afuture performance of a dependent acquisition in response to adetermination that the estimated normalized intensity value exceeds aminimum threshold value (e.g., 0.3) but is less than a maximum thresholdvalue (e.g., 0.7). Similarly, system 400 may schedule a futureperformance of a dependent acquisition in response to a determinationthat the estimated temporal distance is less than a maximum thresholdvalue (e.g., 0.5 minutes) but is greater than a minimum threshold value(e.g., 0.2 minutes). Similarly, system 400 may schedule a futureperformance of a dependent acquisition in response to a determinationthat the selected detection point is located in a rising region but notin a baseline region or an apex region.

In some analyses, multiple different components included in sample 108may co-elute from liquid chromatograph 102 at substantially the sametime. Accordingly, operations 512, 516, and 520 may be performed formultiple different selected m/z.

FIG. 10 shows an illustrative method of performing tandem massspectrometry with LC-MS system 100 and system 400 to efficiently analyzea plurality of components of interest (corresponding to multipledifferent m/z). While FIG. 10 shows illustrative operations according toone embodiment, other embodiments may omit, add to, reorder, and/ormodify any of the operations shown in FIG. 10. FIG. 10 is similar toFIG. 5 except that operations 512, 516, and 520 are replaced in FIG. 10by operations 1002, 1004, and 1006. Accordingly, only operations 1002,1004, and 1006 will be described.

In operation 1002, system 400 extracts a plurality of second data sets514 from first data set 508. Each second data set 514 corresponds to adifferent selected m/z and includes a plurality of detection pointsrepresenting intensity, as detected by mass spectrometer 104, as afunction of retention time for the respective selected m/z.

In operation 1004, system 400 estimates a relative position of aselected detection point included in each respective second data set 514based on each respective second data set 514 and estimation model 518.Each relative position represents a position of the selected detectionpoint to a reference point for the respective selected m/z. Thus, system400 estimates a plurality of relative positions for a plurality ofdifferent selected m/z. Operation 1004 may be performed in any suitableway, including any way described herein.

In operation 1006, a data-dependent action is performed based on theplurality of estimated relative positions. Operation 1006 may beperformed in any suitable way, including any way described herein.

In some examples, the data-dependent action comprises sorting theselected m/z based on an order or ranking of the plurality of estimatedrelative positions and performing the data-dependent action based on thesorting. For example, a sorting rule may specify that dependentacquisitions for a plurality of selected m/z are to be performed inorder of normalized intensity value (e.g., from high to low) and/ortemporal distance. To illustrate, a first selected m/z may have anormalized intensity value of 0.52, a second m/z may have a normalizedintensity value of 0.72, and a third m/z may have a normalized intensityvalue of 0.61. Thus, a dependent acquisition for the second m/z isperformed (or scheduled to be performed) first, a dependent acquisitionfor the third m/z is performed (or scheduled to be performed) second,and a dependent acquisition for the first m/z is performed (or scheduledto be performed) third. In some examples, the dependent acquisition isperformed (or scheduled to be performed) only for the selected m/zhaving an estimated normalized intensity value above (or below) athreshold value.

In some examples, the data-dependent action comprises scheduling futuredependent acquisitions for each selected m/z. For example, system 400may estimate, based on each estimated normalized intensity value, anexpected time at which the intensity value of each selected m/z willreach its maximum intensity value or will be located within a particularregion (e.g., an apex region). System 400 may schedule the dependentacquisitions to be performed at the estimated expected times. In thisway, the order of performing a plurality of dependent acquisitions maybe optimized.

In some examples, the data-dependent action may comprise a filteringoperation. For example, system 400 may perform (or abstain fromperforming) a data-dependent action for the selected m/z having anestimated normalized intensity value that is less than a thresholdvalue, having an estimated temporal distance exceeding a thresholdvalue, or located outside of a particular region. To illustrate, system400 may schedule the future performance of a dependent acquisition foronly those selected m/z having an estimated normalized intensity valuethat is less than a threshold value, having an estimated temporaldistance greater than a threshold time value, or located in a particularregion (e.g., a baseline region). As another illustration, a dependentacquisition may be triggered for only the selected m/z having anestimated normalized intensity value that exceeds a threshold value(e.g., 0.6), having an estimated temporal distance less than a thresholdtime value, or located in a particular region (e.g., a rising region).

In some examples, dynamic exclusion may be applied during a dependentacquisition. Accordingly, estimation model 518 may be configured todetermine a dynamic exclusion window for dynamic exclusion. Referringagain to FIGS. 6 and 7, a width of peak 606 and/or an expected timet_(min) at which the intensity value of peak 606 is expected to reach aminimum value l_(min), as indicated by dashed line 614, may also beestimated based on set 612 of detection points 602 and estimation model518. In the example of FIGS. 6 and 7, Imin is shown to be greater thanzero, but in some embodiments Imin may be set to zero. The estimatedpeak width and/or expected time t_(min) may be used to set the dynamicexclusion window. For example, the dynamic exclusion window may be setfrom expected time t_(max) to expected time t_(min) to excludetriggering a dependent action on the back half of peak 606.

As mentioned, in some examples estimation model 518 comprises a machinelearning model configured to estimate a relative position of a selecteddetection point of a selected m/z. Illustrative methods of training amachine learning model will now be described. FIG. 11 illustrates amethod 1100 for training a machine learning model that may implementestimation model 518. As shown, training data 1102 may be provided to amodel training facility 1104, which may utilize the training data 1102to train an estimation model 1106. The examples that follow describetraining estimation model 1106 to estimate a normalized intensity valueof a selected detection point for a selected m/z. However, the sameprinciples may be applied to train estimation model to estimate atemporal distance of the selected detection point from a reference pointand/or to estimate an elution profile region in which the selecteddetection point is located.

Model training facility 1104 may perform any suitable heuristic,process, and/or operation that may be configured to train a machinelearning model. In some examples, model training facility 1104 may beimplemented by hardware and/or software components (e.g., processors,memories, communication interfaces, instructions stored in memory forexecution by the processors, etc.), such as storage facility 402 and/orprocessing facility 404 of system 400.

Estimation model 1106 may be any suitable type of machine learningmodel, such as a neural network model (e.g., a convolutional neuralnetwork (CNN)), a Boosted Decision Tree regression model, a DecisionForest regression model, a Fast Forest Quantile regression model, and anordinal regression model.

Training data 1102 may be acquired or extracted from data representativeof one or more elution profiles (e.g., a set of LC-MS detection points)for one or more selected m/z. FIG. 12 illustrates a data set that may beincluded in training data 1102. FIG. 12 shows an illustrative XIC 1200for a selected m/z and that may be generated from, or is representativeof, data acquired from a combined separation-mass spectrometry system(e.g., LC-MS system 120). It will be recognized that estimation model1106 may be trained based on XIC 1200 source data without generating XIC1200. As shown, XIC 1200 plots a plurality of detection points 1202 eachrepresenting a detected intensity value of the selected m/z, as detectedby a mass spectrometer during full-spectrum acquisitions, and retentiontime (min). Each successive acquisition adds a new detection point 1202to XIC 1200. Detection points 1202 together form an elution profile 1204of the component associated with the selected m/z. Elution profile 1204includes a peak 1206 having an apex 1208 at which a detected intensityis at a maximum intensity value l_(max), as indicated by dashed line1210.

Training of estimation model 1106 is based on the principle that thenormalized intensity value (or temporal distance or assigned region) ofa selected detection point 1202 (e.g., a ratio of the detected intensityvalue of the selected detection point 1202 to a known referenceintensity value, such as l_(max)) is a function of the detectedintensity levels of one or more historical detection points 1202 fromthe same experiment. Accordingly, training data 1102 applied to modeltraining facility 1104 comprises a series of input vectors for theselected m/z, each input vector having detected intensity values for adistinct set of detection points 1202. Each input vector may compriseany distinct set of detection points 1202 as may serve a particularimplementation. For example, a first input vector may comprise detectedintensity values for a first set 1212-1 of detection points 1202, asecond input vector may comprise intensity values for a second set1212-2 of detection points 1202, a third input vector may compriseintensity values for a third set 1212-3 of detection points 1202, and soon.

In the example of FIG. 12, each input vector includes intensity valuesfor ten detection points 1202. However, each input vector may includeintensity values for any other suitable number of detection points 1202(e.g., 6, 12, 24, etc.). Alternatively, each input vector may includeintensity values for all detection points 1202 included in one or moreprior input vectors.

In some examples, detection points 1202 may not be evenly spaced alongthe time axis. To simplify training of estimation model 1106, detectionpoints 1202 may be corrected (such as by interpolation) to a fixed anduniform time spacing (e.g., 1 second).

Any number of input vectors for a selected m/z may be applied to modeltraining facility 1104. In some examples, the number of input vectors isselected so as to encompass at least a full half-width of peak 1206. Inthe example of FIG. 12, five input vectors having intensity values forten detection points each would encompass at least a full half-width ofpeak 1206. Alternatively, the number of input vectors may be selected soas to encompass a full width of peak 1206. Input vectors that encompassthe full width of peak 1206 may be used to train estimation model 1106to estimate an expected time t_(min) at which the intensity value of apeak is expected to reach a minimum value, which may be used in settinga dynamic exclusion window.

A particular detection point 1202 is selected for each input vector as aselected detection point. Any detection point 1202 included within eachrespective input vector may be selected. In the examples that follow,the selected detection point 1202 for each input vector is theright-most (most recent) detection point 1202 included in the respectiveinput vector. For example, the first input vector includes detectionpoint 1202-1 as the selected detection point, the second input vectorincludes detection point 1202-2 as the selected detection point, and thethird input vector includes detection point 1202-3 as the selecteddetection point. In alternative examples, the selected detection point1202 is not the right-most detection point but may be any other suitabledetection point (e.g., detection point 1202-1 is the selected detectionpoint for the second input vector defined by set 1212-2, detection point1202-2 is the selected detection point for the third input vectordefined by set 1212-3, etc.).

Training data 1102 also includes the known desired output values fromestimation model 1106. The known desired output values comprise thenormalized intensity values of each selected detection point 1202 foreach input vector (or the temporal distance values of each selecteddetection point 1202 for each input vector or a region of elutionprofile 1204 in which each selected detection point 1202 is located).The normalized intensity values are known because the referenceintensity value (e.g., l_(max)) is known. The known output values may beused for supervised training of estimation model 1106.

To simplify training of estimation model 1106, detection points 1202 mayin some examples be corrected based on the known reference intensityvalue. That is, the detected intensity value of each detection point1202 may be normalized based on the known reference value.

In the examples described above, the reference intensity value is theexpected maximum intensity value l_(max) at apex 1208 of peak 1206.However, any other normalization scheme may be used, and the referenceintensity value may be any other suitable reference value, such as aknown running average intensity value for the selected m/z, a globalmaximum intensity value for multiple different m/z, a recent maximumintensity value for the selected m/z, etc.

In some examples, training data 1102 may be split into two sets of data,such that a first set of training data may be used for trainingestimation model 1106 and a second set of training data may be used toscore estimation model 1106. For example, training data 1102 may besplit so that a first percentage (e.g., 75%) of the input vectors may beused as the training set for training estimation model 1106, and asecond percentage (e.g., 25%) of the input vectors may be used as thescoring set to generate an accuracy score for estimation model 1106.

During a training phase, model training facility 1104 may run one ormore sessions to train estimation model 1106 based on training data 1102to estimate a normalized intensity value of a selected detection point1202. Model training facility 1104 may also run one or more sessions totrain estimation model 1106 based on training data 1102 to estimate atemporal distance of the selected detection point to a reference time,e.g., an expected time t_(max) at which the intensity value of anelution profile is expected to reach maximum intensity value (e.g., anapex of the elution profile) and/or an expected time t_(min) at whichthe intensity value of the elution profile is expected to reach aminimum intensity value. Model training facility 1104 may also run oneor more sessions to train estimation model 1106 based on training data1102 to estimate a region in which the selected detection point islocated. Model training facility 1104 may use any suitable machinelearning technology or algorithm to perform operations to facilitatelearning, by a machine learning model, of how to fit the machinelearning model to the detected intensity values within the first set oftraining data 1102.

Completion of a training phase, by model training facility 1104, mayresult in trained estimation model 1106 that is configured to estimate arelative position of a selected detection point. The trained estimationmodel 1106 may be stored in a data store, such as storage facility 402,and may be executed during runtime by any suitable computing component,including processing facility 404.

As mentioned, training data 1102 may include data for multiple differentselected m/z. If data for multiple different selected m/z and/or underdifferent chromatography conditions is acquired from survey acquisitionswith a period short enough to characterize the chromatographic peaks(the Nyquist limit), trained estimation model 1106 may be applicable toexperimental data sets that may use different chromatography conditionsand have different peak widths. The Nyquist limit for a Gaussian curveis six points. Thus, if training data 1102 includes six detection pointsacross a chromatographic peak, interpolation to a fixed time spacingbetween the sampled points could be robust.

In alternative examples, estimation model 1106 may be trained based ontraining data 1102 configured for a specific application, such as aspecific m/z, specific chromatographic conditions, a specific sampletype, etc. In such examples, estimation model 1106 could be trainedafter acquiring data for an initial priming experiment, and estimationmodel 1106 could be used thereafter only for subsequent iterations ofthat specific experiment.

In some examples, estimation model 1106 may be refined or furthertrained in real time (e.g., during an experiment), such as if it isfound that estimated normalized intensity values or estimated temporaldistances are deviating significantly (e.g., by a predetermined amount)from the actual values (which may be subsequently detected as theexperiment progresses).

Various modifications may be made to the systems and methods describedherein without departing from the scope and principles of the conceptsdescribed herein. For instance, in the examples described above thetraining of an estimation model and estimation of a relative position ofa selected detection point is based on a plurality of detection pointsfor a selected m/z (e.g., an XIC). In some modifications, training of anestimation model and estimation of a relative position of a selecteddetection point may be based on the signals of multiple m/z values orall m/z values (e.g., a total ion current (TIC) chromatogram).

In additional modifications, a separation device (e.g., a liquidchromatograph, a gas chromatograph, a capillary electrophoresis device,etc.) and/or a mass spectrometer (e.g., mass spectrometer 124) mayinclude or may be coupled with an ion mobility analyzer, and dataacquired by the ion mobility analyzer may be used to train an estimationmodel and estimate a relative position of a selected detection point ina manner similar to the methods described above for data acquired by themass spectrometer. For example, a first set of data acquired with an ionmobility analyzer and a mass analyzer may include a series of massspectra including intensity values of ions produced from the samplecomponents as a function of m/z and/or ion mobility of the ions (e.g., acollision cross-section (CCS) of the ions). A second set of data may beextracted from the first set of data. The extracted second set of datamay include a plurality of detection points representing intensity, asdetected by the mass analyzer, as a function of time for a selected m/zand/or a selected CCS or range of CCS. The second set of data may beused in any way described herein, such as to train an estimation modeland to estimate, during a DDA procedure, a relative position of aselected detection point for the selected CCS or range of CCS.

In certain embodiments, one or more of the systems, components, and/orprocesses described herein may be implemented and/or performed by one ormore appropriately configured computing devices. To this end, one ormore of the systems and/or components described above may include or beimplemented by any computer hardware and/or computer-implementedinstructions (e.g., software) embodied on at least one non-transitorycomputer-readable medium configured to perform one or more of theprocesses described herein. In particular, system components may beimplemented on one physical computing device or may be implemented onmore than one physical computing device. Accordingly, system componentsmay include any number of computing devices, and may employ any of anumber of computer operating systems.

In certain embodiments, one or more of the processes described hereinmay be implemented at least in part as instructions embodied in anon-transitory computer-readable medium and executable by one or morecomputing devices. In general, a processor (e.g., a microprocessor)receives instructions, from a non-transitory computer-readable medium,(e.g., a memory, etc.), and executes those instructions, therebyperforming one or more processes, including one or more of the processesdescribed herein. Such instructions may be stored and/or transmittedusing any of a variety of known computer-readable media.

A computer-readable medium (also referred to as a processor-readablemedium) includes any non-transitory medium that participates inproviding data (e.g., instructions) that may be read by a computer(e.g., by a processor of a computer). Such a medium may take many forms,including, but not limited to, non-volatile media, and/or volatilemedia. Non-volatile media may include, for example, optical or magneticdisks and other persistent memory. Volatile media may include, forexample, dynamic random access memory (“DRAM”), which typicallyconstitutes a main memory. Common forms of computer-readable mediainclude, for example, a disk, hard disk, magnetic tape, any othermagnetic medium, a compact disc read-only memory (“CD-ROM”), a digitalvideo disc (“DVD”), any other optical medium, random access memory(“RAM”), programmable read-only memory (“PROM”), electrically erasableprogrammable read-only memory (“EPROM”), FLASH-EEPROM, any other memorychip or cartridge, or any other tangible medium from which a computercan read.

FIG. 13 shows an illustrative computing device 1300 that may bespecifically configured to perform one or more of the processesdescribed herein. As shown in FIG. 13, computing device 1300 may includea communication interface 1302, a processor 1304, a storage device 1306,and an input/output (“I/O”) module 1308 communicatively connected one toanother via a communication infrastructure 1310. While an illustrativecomputing device 1300 is shown in FIG. 13, the components illustrated inFIG. 13 are not intended to be limiting. Additional or alternativecomponents may be used in other embodiments. Components of computingdevice 1300 shown in FIG. 13 will now be described in additional detail.

Communication interface 1302 may be configured to communicate with oneor more computing devices. Examples of communication interface 1302include, without limitation, a wired network interface (such as anetwork interface card), a wireless network interface (such as awireless network interface card), a modem, an audio/video connection,and any other suitable interface.

Processor 1304 generally represents any type or form of processing unitcapable of processing data and/or interpreting, executing, and/ordirecting execution of one or more of the instructions, processes,and/or operations described herein. Processor 1304 may performoperations by executing computer-executable instructions 1312 (e.g., anapplication, software, code, and/or other executable data instance)stored in storage device 1306.

Storage device 1306 may include one or more data storage media, devices,or configurations and may employ any type, form, and combination of datastorage media and/or device. For example, storage device 1306 mayinclude, but is not limited to, any combination of the non-volatilemedia and/or volatile media described herein. Electronic data, includingdata described herein, may be temporarily and/or permanently stored instorage device 1306. For example, data representative ofcomputer-executable instructions 1312 configured to direct processor1304 to perform any of the operations described herein may be storedwithin storage device 1306. In some examples, data may be arranged inone or more databases residing within storage device 1306.

I/O module 1308 may include one or more I/O modules configured toreceive user input and provide user output. One or more I/O modules maybe used to receive input for a single virtual experience. I/O module1308 may include any hardware, firmware, software, or combinationthereof supportive of input and output capabilities. For example, I/Omodule 1308 may include hardware and/or software for capturing userinput, including, but not limited to, a keyboard or keypad, atouchscreen component (e.g., touchscreen display), a receiver (e.g., anRF or infrared receiver), motion sensors, and/or one or more inputbuttons.

I/O module 1308 may include one or more devices for presenting output toa user, including, but not limited to, a graphics engine, a display(e.g., a display screen), one or more output drivers (e.g., displaydrivers), one or more audio speakers, and one or more audio drivers. Incertain embodiments, I/O module 1308 is configured to provide graphicaldata to a display for presentation to a user. The graphical data may berepresentative of one or more graphical user interfaces and/or any othergraphical content as may serve a particular implementation.

In some examples, any of the systems, computing devices, and/or othercomponents described herein may be implemented by computing device 1300.For example, storage facility 202 may be implemented by storage device1306, and processing facility 204 may be implemented by processor 1304.

It will be recognized by those of ordinary skill in the art that while,in the preceding description, various illustrative embodiments have beendescribed with reference to the accompanying drawings. It will, however,be evident that various modifications and changes may be made thereto,and additional embodiments may be implemented, without departing fromthe scope of the invention as set forth in the claims that follow. Forexample, certain features of one embodiment described herein may becombined with or substituted for features of another embodimentdescribed herein. The description and drawings are accordingly to beregarded in an illustrative rather than a restrictive sense.

What is claimed is:
 1. A method of performing tandem mass spectrometrycomprising: supplying a sample to a chromatography column; directingcomponents included in the sample and eluting from the chromatographycolumn to a mass spectrometer; acquiring a series of mass spectraincluding intensity values of ions produced from the components as afunction of m/z of the ions; extracting, from the series of massspectra, a plurality of detection points representing intensity as afunction of time for a selected m/z; estimating, based on the pluralityof detection points extracted from the series of mass spectra, arelative position of a selected detection point included in theplurality of detection points, the relative position of the selecteddetection point representing a position of the selected detection pointrelative to an expected reference point; and performing, at the massspectrometer and based on the estimated relative position, a dependentacquisition for the selected m/z.
 2. The method of claim 1, wherein therelative position of the selected detection point comprises a normalizedintensity value of the selected detection point, the normalizedintensity value representing a ratio of the detected intensity value ofthe selected detection point to an expected maximum intensity value forthe selected m/z.
 3. The method of claim 2, further comprising:determining that the normalized intensity value exceeds a thresholdvalue; wherein the dependent acquisition is performed in response to thedetermining that the normalized intensity value exceeds the thresholdvalue.
 4. The method of claim 3, wherein the threshold value is betweenabout 0.5 and about 1.0.
 5. The method of claim 3, wherein the thresholdvalue is between about 0.8 and about 1.0.
 6. The method of claim 1,wherein the relative position of the selected detection point comprisesa temporal distance of the selected detection point to an expected timepoint for the selected m/z.
 7. The method of claim 1, wherein therelative position of the selected detection point comprises a region ofan expected elution profile for the selected m/z and in which theselected detection point is located.
 8. The method of claim 1, whereinthe dependent acquisition for the selected m/z comprises an MS/MSacquisition.
 9. The method of claim 1, wherein the performing thedependent acquisition comprises: scheduling the dependent acquisitionfor a future time based on the relative position of the selecteddetection point; and performing the dependent acquisition at the futuretime.
 10. The method of claim 9, wherein the scheduling the dependentacquisition comprises: estimating, based on the estimated relativeposition, an expected time of a maximum intensity value for the selectedm/z; wherein the future time comprises the estimated expected time ofthe maximum intensity value for the selected m/z.
 11. An apparatus forperforming tandem mass spectrometry, comprising: a mass spectrometerconfigured to receive components included in a sample and eluting from achromatography column and analyze ions produced from the components; anda computing device configured to: acquire, from the mass spectrometer, aseries of mass spectra including intensity values of ions produced fromthe components as a function of m/z of the ions; extract, from theseries of mass spectra, a plurality of detection points detected by themass spectrometer over time for each of a plurality of differentselected m/z; estimate, based on the plurality of detection points foreach respective selected m/z, a relative position of a selecteddetection point included in each plurality of detection points, eachrelative position representing a position of the selected detectionpoint relative to an expected reference point for the respectiveselected m/z; and control the mass spectrometer to perform, based on theestimated relative positions, a plurality of dependent acquisitions. 12.The apparatus of claim 11, wherein: each estimated relative positioncomprises a normalized intensity value of the respective selecteddetection point, the normalized intensity value representing a ratio ofthe detected intensity value of the selected detection point to anexpected maximum intensity value for the selected m/z; and the computingdevice is configured to control the mass spectrometer to perform theplurality of dependent acquisitions based on a numerical order of theestimated normalized intensity values.
 13. The apparatus of claim 12,wherein the plurality of dependent acquisitions comprises a dependentacquisition for each selected m/z for which a corresponding selecteddetection point has a normalized intensity value exceeding a thresholdvalue.
 14. The apparatus of claim 13, wherein the threshold value isbetween about 0.5 and about 1.0.
 15. The apparatus of claim 13, whereinthe threshold value is between about 0.8 and about 1.0.
 16. Theapparatus of claim 12, wherein the controlling the mass spectrometer toperform the plurality of dependent acquisitions comprises: schedulingeach of the plurality of dependent acquisitions for a different futuretime based on a numerical order of the normalized intensity values; andcontrolling the mass spectrometer to perform each of the plurality ofdependent acquisitions at the respective future time.
 17. The apparatusof claim 16, wherein the scheduling each of the plurality of dependentacquisitions comprises: estimating, based on the estimated normalizedintensity values, an expected time of a maximum intensity value for eachselected m/z; wherein each respective future time comprises theestimated expected time of the maximum intensity value for therespective selected m/z.
 18. A non-transitory computer-readable mediumstoring instructions that, when executed, cause a processor of acomputing device to: acquire a first data set comprising a series ofmass spectra including intensity values of ions produced from analyteseluting from a chromatography column as a function of m/z of the ions;extract a second data set from the first data set, the second data setincluding a plurality of detection points representing intensity as afunction of time for a selected mass-to-charge ratio (m/z); estimate,based on the second data set, a relative position of a selecteddetection point included in the second data set, the relative positionof the selected detection point representing a position of the selecteddetection point relative to an expected reference point for the selectedm/z; and control, based on the estimated relative position, the massspectrometer to perform a data-dependent action.
 19. The non-transitorycomputer-readable medium of claim 18, wherein the relative position ofthe selected detection point comprises a normalized intensity value ofthe selected detection point, the normalized intensity valuerepresenting a ratio of the detected intensity value of the selecteddetection point to an expected maximum intensity value for the selectedm/z.
 20. The non-transitory computer-readable medium of claim 19,wherein the controlling the mass spectrometer to perform thedata-dependent action comprises: determining that the normalizedintensity value exceeds a threshold value; and controlling, in responseto the determining that the normalized intensity value exceeds thethreshold value, the mass spectrometer to perform the data-dependentaction.
 21. The non-transitory computer-readable medium of claim 20,wherein the threshold value is between about 0.5 and about 1.0.
 22. Thenon-transitory computer-readable medium of claim 20, wherein thethreshold value is between about 0.8 and about 1.0.
 23. Thenon-transitory computer-readable medium of claim 18, wherein therelative position of the selected detection point comprises a temporaldistance of the selected detection point to an expected time point forthe selected m/z.
 24. The non-transitory computer-readable medium ofclaim 18, wherein the relative position of the selected detection pointcomprises a region of an expected elution profile for the selected m/zand in which the selected detection point is located.
 25. Thenon-transitory computer-readable medium of claim 18, wherein theestimating the relative position of the selected detection point isbased on intensity values of detection points included in a slidingwindow of the second data set, the sliding window including a currentdetection point.
 26. The non-transitory computer-readable medium ofclaim 25, wherein the selected detection point comprises the currentdetection point.
 27. The non-transitory computer-readable medium ofclaim 18, wherein the data-dependent action comprises performing tandemmass spectrometry.
 28. The non-transitory computer-readable medium ofclaim 18, wherein the controlling the mass spectrometer to perform thedata-dependent action comprises scheduling the mass spectrometer toperform tandem mass spectrometry for the selected m/z at a future time.29. The non-transitory computer-readable medium of claim 28 wherein thescheduling the mass spectrometer to perform tandem mass spectrometry forthe selected m/z at the future time comprises: estimating, based on theestimated relative position of the selected detection point, an expectedtime of a maximum intensity value for the selected m/z; wherein thefuture time comprises the estimated expected time of the maximumintensity value for the selected m/z.
 30. A system comprising: achromatography column configured to receive a sample and separatecomponents included in the sample; a mass spectrometer configured toreceive the components eluting from the chromatography column andanalyze ions produced from the components; and a computing deviceconfigured to: acquire a series of mass spectra including intensityvalues of ions produced from the components as a function of m/z of theions; extract, from the series of mass spectra, a plurality of detectionpoints representing intensity as a function of time for a selected m/z;estimate, based on the plurality of detection points extracted from theseries of mass spectra, a relative position of a selected detectionpoint included in the plurality of detection points, the relativeposition of the selected detection point representing a position of theselected detection point relative to an expected reference point; andcontrol, based on the estimated relative position, the mass spectrometerto perform a dependent acquisition for the selected m/z.
 31. A method ofperforming tandem mass spectrometry comprising: supplying a sample to achromatography column; directing components included in the sample andeluting from the chromatography column to a mass spectrometer comprisingan ion mobility analyzer and a mass analyzer; acquiring a series of massspectra including intensity values of ions produced from the componentsas a function of a collision cross-section (CCS) of the ions;extracting, from the series of mass spectra, a plurality of detectionpoints representing intensity as a function of time for a selected CCS;estimating, based on the plurality of detection points extracted fromthe series of mass spectra, a relative position of a selected detectionpoint included in the plurality of detection points, the relativeposition of the selected detection point representing a position of theselected detection point relative to an expected reference point; andperforming, at the mass spectrometer and based on the estimated relativeposition, a dependent acquisition for the selected CCS.